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At the end of the course, participants will be able to:

• Analyze security requirements in hybrid networks to work toward an enterprise-wide, zero-trust security architecture with advanced secure cloud and virtualization solutions.
• Address advanced threat management, vulnerability management, risk mitigation, incident response tactics and digital forensic analysis.
• Prove an organization's overall cybersecurity resiliency metric and compliance to regulations.
• Configure for endpoint security controls, enterprise mobility, cloud/hybrid environments and enterprise-wide PKI.

• Design and implement generative artificial intelligence models using the Azure platform.
• Build, evaluate, and optimize AI models.
• Manage and Monitor AI workflows.
• Integrate AI solutions into business processes.
• Develop proficiency in leveraging Azure's pre-built AI capabilities.

• Describe considerations for AI-enabled application development
• Create, configure, deploy, and secure Azure Cognitive Services
• Develop applications that analyze text
• Develop speech-enabled applications
• Create applications with natural language understanding capabilities
• Create QnA applications
• Create conversational solutions with bots
• Use computer vision services to analyze images and videos

At the end of the course, participants should be able to:

• Design a solution that analyzes your business forms by using Azure AI Document Intelligence.
• Create a solution that analyzes common documents by using Document Intelligence.
• Create a solution that analyses different custom form types by using Document Intelligence.
• Include an Azure AI Document Intelligence service as a custom skill in an Azure Cognitive Search pipeline.

At the end of the AI-3003 training, participants should be able to:

• Deploy a language resource and use prebuilt models.
• Create a custom text classification solution.
• Create a custom named entity recognition (NER) solution.

At the end of this training, participants should be able to:

• Create a computer vision resource
• Analyze images
• Create a custom image analysis model
• Train and evaluate the model
• Consume a custom model

At the end of this training, participants should be able to:

• Navigate and utilize Azure AI Studio’s core features and capabilities
• Provision and manage AI resources and projects within Azure AI Studio
• Develop and manage language model applications using prompt flow
• Implement RAG to enhance language models with custom data
• Develop responsible generative AI solutions, identifying and mitigating potential harms

At the end of this course, participants should be able to:

• Leverage AI tools and resources for your business
• Create business value from AI
• Embrace responsible AI principles and practices
• Scale AI in your organization

At the end of this course, participants should be able to:

• Comprehend the fundamentals of generative AI and its role in AI development.
• Explore language models and their application in intelligent tasks.
• Describe examples of effective copilots and how to create good prompts.
• Get hands-on experience with Microsoft Copilot Studio to create, test, and analyze copilots.
• Master the core features and capabilities of Azure AI Studio.
• Provision and manage Azure AI resources using Azure AI Studio.

At the end of this course, participants should be able to:

• Analyze the basics of generative AI and its integration with Azure Database for PostgreSQL.
• Implement practical examples of generative AI models.
• Enable semantic search in Azure Database for PostgreSQL:
• Learn to integrate and optimize semantic search functionalities.
• Explore techniques to improve search accuracy and relevance.